





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class      lowerCamelCase(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			BioGptTokenizer
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _A     =							[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             '<unk>',
         ]
         _A     =							dict(zip(snake_case_				,     range(len(snake_case_							)							)							)							)
         _A     =							['l o 123', 'lo w 1456', 'e r</w> 1789', '']

         _A     =							os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES['vocab_file']							)
         _A     =							os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES['merges_file']							)
         with open(self.vocab_file				,     'w'							) as fp:
             fp.write(json.dumps(snake_case_							)							)
         with open(self.merges_file				,     'w'							) as fp:
             fp.write('\n'.join(snake_case_							)							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							'lower newer'
         _A     =							'lower newer'
         return input_text, output_text




     def        lowerCAmelCase__		(    self							):
         _A     =							BioGptTokenizer(self.vocab_file				,     self.merges_file							)

         _A     =							'lower'
         _A     =							['low', 'er</w>']
         _A     =							tokenizer.tokenize(snake_case_							)
         self.assertListEqual(snake_case_				,     snake_case_							)

         _A     =							tokens + ['<unk>']
         _A     =							[14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_							)				,     snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							BioGptTokenizer.from_pretrained('microsoft/biogpt'							)

         _A     =							tokenizer.encode('sequence builders'				,     add_special_tokens=snake_case_							)
         _A     =							tokenizer.encode('multi-sequence build'				,     add_special_tokens=snake_case_							)

         _A     =							tokenizer.build_inputs_with_special_tokens(snake_case_							)
         _A     =							tokenizer.build_inputs_with_special_tokens(snake_case_				,     snake_case_							)

         self.assertTrue(encoded_sentence == [2] + text							)
         self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)












'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



